ADEPT: Architecture-Driven Energy-Efficient CNN Fine-Tuning on PIM Accelerators

ADEPT addresses the problem of high computational and memory overhead in CNN fine-tuning on PIM accelerators, caused by frequent off-chip memory access for intermediate activations. The method introduces a hardware-aware framework that adaptively trains models using a novel metric balancing gradient-based sensitivity with architecture-specific Energy-Delay Product (EDP). Experimental evidence shows ADEPT reduces total trainable parameters and off-chip data access during fine-tuning with minimal loss in predictive accuracy compared to full-parameter fine-tuning. This matters because it enables energy-efficient fine-tuning on PIM accelerators, making edge deployment of CNNs more practical without sacrificing model accuracy.